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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Retirement, Disability, and Employment
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,535 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,535 works in the cohort · of 4,299,418page 1 of 31

Labels cover 3 of 1,535 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,535 of 1,535 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Ageism and the Older Worker: A Scoping Review
Kelly M. Harris, Sarah Krygsman, Jessica Waschenko, Debbie Laliberté Rudman
2016· review· en· The Gerontologist· Social Sciences
machine prediction:candidate · noneconsensus · none
208
citations
affunlabeled
Return to work after young stroke: A systematic review
Jodi D. Edwards, Arunima Kapoor, Elizabeth Linkewich, Richard H. Swartz
2017· review· en· International Journal of Stroke· Social Sciences
machine prediction:candidate · noneconsensus · none
195
citations
affunlabeled
From Midlife to Early Old Age
Markus Jokela, Jane E. Ferrie, David Gimeno, Tarani Chandola, Martin J. Shipley, Jenny Head +4 more
2010· article· en· Epidemiology· Social Sciences
machine prediction:candidate · noneconsensus · none
190
citations
affunlabeled
More Caregiving, Less Working
Yeonjung Lee, Fengyan Tang
2013· article· en· Journal of Applied Gerontology· Social Sciences
machine prediction:candidate · noneconsensus · none
184
citations
affaboutunlabeled
Time Use at Older Ages
Anne H. Gauthier, Timothy M. Smeeding
2003· article· en· Research on Aging· Social Sciences
machine prediction:candidate · noneconsensus · none
168
citations
afffundaboutunlabeled
A systematic review of physician retirement planning
Michelle Pannor Silver, Angela D. Hamilton, Aviroop Biswas, Natalie Warrick
2016· review· en· Human Resources for Health· Social Sciences
machine prediction:candidate · noneconsensus · none
154
citations
affunlabeled
The Bridge to Retirement
Gerry Kerr, Marjorie Armstrong‐Stassen
2011· article· en· The Journal of Entrepreneurship· Social Sciences
machine prediction:candidate · noneconsensus · none
114
citations
aboutno affunlabeled
Health and safety of the older worker
A Farrow, Frances Reynolds
2011· review· en· Occupational Medicine· Social Sciences
machine prediction:candidate · noneconsensus · none
106
citations
affaboutunlabeled
The importance of music to seniors.
Annabel J. Cohen, Betty A. Bailey, Thomy Nilsson
2002· article· en· Psychomusicology Music Mind and Brain· Social Sciences
machine prediction:candidate · noneconsensus · none
101
citations
affunlabeled
Older Americans Would Work Longer if Jobs Were Flexible
John Ameriks, Joseph Briggs, Andrew Caplin, Min Joon Lee, Matthew D. Shapiro, Christopher Tonetti
2020· article· en· American Economic Journal Macroeconomics· Social Sciences
machine prediction:candidate · noneconsensus · none
83
citations
affunlabeled
Age-related bias and artificial intelligence: a scoping review
Charlene H. Chu, Simon Donato‐Woodger, Shehroz S. Khan, Rune Nyrup, Kathleen Leslie, Alexandra Lyn +4 more
2023· review· en· Humanities and Social Sciences Communications· Social Sciences
machine prediction:candidate · noneconsensus · none
81
citations

How this was built: Screen · Findings · About